Rapporto diseguaglianze e povertà

packages <- c('ggbump','tidyverse','laeken','MetBrewer','sf','ggiraph')
install.packages(setdiff(packages, rownames(installed.packages()))) 
rm(packages)
library(ggbump)
library(tidyverse)
library(laeken)
library(MetBrewer)
library(sf)
library(ggiraph)
# import pdata
pdata <- readRDS('SHIWpdata.rds')
# Inequality indexes -----------------------------------------------------------
## Gini - Income ----
### Over regions by year
as_tibble(gini(pdata$eqhincome,
     weights = pdata$peso,
     years = pdata$anno,
     breakdown = pdata$ireg,
     na.rm = T
)[['valueByStratum']]) -> giniInc

giniInc$stratum <- gsub(' - ', '-', giniInc$stratum)
### ranking
giniInc |> 
  group_by(year) |> 
  mutate(
    rank = rank(value)
  ) -> giniInc
### rounding off value
giniInc$value <- round(giniInc$value, 2)

### ranking5
giniInc$rank5 <- case_match(giniInc$rank,
                            1:4 ~ 1,
                            5:8 ~ 2,
                            9:12 ~ 3,
                            13:16 ~ 4,
                            17:20 ~ 5
                            )
### Total by year
as_tibble_col(gini(pdata$eqhincome,
     weights = pdata$peso,
     years = pdata$anno,
     na.rm = T
)[["value"]]) -> giniIncTot

giniIncTot$stratum <- 'Italia'
giniIncTot$year <- c(2000, 2002, 2004, 2008, 2010, 2012, 2014, 2016, 2020)
giniIncTot$rank <- NA

giniInc <- rbind(giniInc, giniIncTot)
rm(giniIncTot)


## Gini - wealth ----
### Over regions by year
as_tibble(gini(pdata$pcwealth,
               weights = pdata$peso,
               years = pdata$anno,
               breakdown = pdata$ireg,
               na.rm = T
)[['valueByStratum']]) -> giniW

giniW$stratum <- gsub(' - ', '-', giniW$stratum)

### ranking
giniW |> 
  group_by(year) |> 
  mutate(
    rank = rank(value)
  ) -> giniW
### ranking5
giniW$rank5 <- case_match(giniW$rank,
                            1:4 ~ 1,
                            5:8 ~ 2,
                            9:12 ~ 3,
                            13:16 ~ 4,
                            17:20 ~ 5
)

### rounding off value
giniW$value <- round(giniW$value, 2)

### Total by year
as_tibble_col(gini(pdata$pcwealth,
                   weights = pdata$peso,
                   years = pdata$anno,
                   na.rm = T
)[["value"]]) -> giniWTot

giniWTot$stratum <- 'Italia'
giniWTot$year <- c(2000, 2002, 2004, 2008, 2010, 2012, 2014, 2016, 2020)
giniWTot$rank <- NA

giniW <- rbind(giniW, giniWTot)
rm(giniWTot)



# Poverty  ---------------------------------------------------------------------

## Head count ----
### Over region by year
pdata |> 
  group_by(anno, ireg) |> 
  count(pov) -> pov
pdata |> 
  group_by(anno, ireg) |> 
  count(!pov) -> pov$'!pov'
pov |> 
  mutate('!pov' = `!pov`$n) |> 
  filter(pov == 1) |> 
  mutate(pov = n) |> 
  select(!n) -> pov

pov |> 
  mutate(hCount = pov/(sum(pov, `!pov`))) -> pov

### Ranking
pov |> 
  group_by(anno) |> 
  mutate(
    rankHCount = 21-rank(hCount)
  ) -> pov

## Poverty intensity ----
### povLine
pdata |> 
  group_by(anno) |> 
  summarise(povLine = weightedMedian(eqhincome,
                                     weights = peso)*0.6) -> povLine
pov <- left_join(pov, povLine)
rm(povLine)

### Poverty gap
avPoor <- pdata |>
  filter(pov == 1) |> 
  group_by(anno, ireg) |>
  summarise(avPoor = weightedMean(eqhincome, weights = peso))
pov <- left_join(pov, avPoor)
rm(avPoor)
pov$povGapIndex <- pov$hCount * (pov$povLine - pov$avPoor)/pov$povLine

### Ranking
pov |> 
  group_by(anno) |> 
  mutate(
    rankPovGap = 21-rank(povGapIndex)
  ) -> pov

## LPM risk of being in poverty ----
pdata <- within(pdata, cfedu <- relevel(factor(cfedu), ref = 'Specializzazione post-laurea'))
pdata <- within(pdata, cfsex <- relevel(factor(cfsex), ref = 'Maschile'))
pdataReg <- pdata |> 
  filter(anno == 2020)

### Regression models
lpm0 <- lm(pov ~ factor(cfedu),
   weights = pesopop,
   data = pdataReg)
lpm1 <- lm(pov ~ factor(cfedu) + factor(cfsex) + factor(cfclass),
           weights = pesopop,
           data = pdataReg)

### Harvesting results
lpm0results <- as_tibble(summary(lpm0)[["coefficients"]])
lpm0results <- cbind(lpm0results, confint(lpm0, level=0.95)) #adding CIs

lpm1results <- as_tibble(summary(lpm1)[["coefficients"]])
lpm1results <- cbind(lpm1results, confint(lpm1, level=0.95)) #adding CIs
lpm0results <- lpm0results %>% setNames(paste0('0.', names(.)))

### Cleaning results
lpm0results <- lpm0results |> 
  rownames_to_column(var = 'reg')
lpm1results <- lpm1results |> 
  rownames_to_column(var = 'reg')

lpmresults <- full_join(lpm0results, lpm1results)
rm(lpm0results, lpm1results, lpm0, lpm1)

lpmresults['reg'][lpmresults['reg'] == '(Intercept)'] <- ')Intercept'

lpmresults <- lpmresults |> 
  mutate(reg = str_split_fixed(reg, fixed(')'), n = Inf)) 

lpmresults <- within(lpmresults, reg <- reg[,2])

lpmresults$'0.star' <- ifelse(lpmresults$`0.Pr(>|t|)` <= 0.001, '***',
                            ifelse(lpmresults$`0.Pr(>|t|)` <= 0.01, '**',
                                   ifelse(lpmresults$`0.Pr(>|t|)` <= 0.05, '*',
                                          ifelse(lpmresults$`0.Pr(>|t|)` <= 0.1, '.', ''))))

lpmresults$star<- ifelse(lpmresults$`Pr(>|t|)` <= 0.001, '***',
                              ifelse(lpmresults$`Pr(>|t|)` <= 0.01, '**',
                                     ifelse(lpmresults$`Pr(>|t|)` <= 0.05, '*',
                                            ifelse(lpmresults$`Pr(>|t|)` <= 0.1, '.', ''))))

lpmresults <- lpmresults |> 
  relocate('0.star', .before = Estimate)

lpmresults$vars <- c('Intercetta', 'Titolo di studio', 'Titolo di studio', 'Titolo di studio', 'Titolo di studio', 'Titolo di studio', 'Sesso', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale', 'Settore economico dell\'occupazione principale')

RxR - una Regione per Restare commissioned a research project on economic inequality in Italy, with a specific focus Umbria, its main region of interest. The complete report is available at report link (in Italian), while the source code is available on github.

Below is a selection of the data visualization work involved (translated to English). The project was conducted using R, interactive plots and maps are realised using the ggiraph package.

Mapping inequality and poverty

##### import sf
regMap <- readRDS('regMap.rds')

##### merge Gini tibbles with sf data
incMap <- left_join(giniInc, regMap, by = join_by(stratum == DEN_REG))

##### ggiraph ready map
incGiniGG <- incMap |> 
  filter(year == 2000 | year == 2010 |year == 2020) |> 
  drop_na(rank) |> 
  ggplot() +
  geom_sf_interactive(aes(geometry = geometry, fill = value, data_id = stratum, tooltip = value), colour = 'black') +
  facet_wrap(vars(year)) +
  labs(x = NULL, y = NULL,
       title = 'Gini index by region',
       subtitle = 'Equivalent household income',
       caption = 'Data: Bank of Italy. Elaborated by Lorenzo Mattioli - Una Regione per Restare') +
  theme_minimal(base_family = 'Helvetica') +
  scale_fill_viridis_c(direction = -1, limits = c(15, 40), option = 'mako') +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        panel.grid = element_blank(),
        legend.title = element_blank(),
        plot.title = element_text(size = 15,
                                  hjust = .5),
        plot.subtitle = element_text(hjust = .5),
        plot.caption = element_text(size = 9,
                                    hjust = .5))


##### interactive map
girafe(ggobj = incGiniGG,
       width_svg = 13,
       options = list(
         opts_hover(css = ''), ## CSS code of line we're hovering over
         opts_hover_inv(css = "opacity:0.3;"), ## CSS code of all other lines
         opts_tooltip(css = "background-color:white;
                      color:black;
                      font-family:Helvetica;
                      font-style:empty;
                      padding:8px;
                      border-radius:10px;",
                      use_cursor_pos = T),
         opts_toolbar(position = 'bottomright')
       ))

pov$ireg <- gsub(' - ', '-', pov$ireg)
povMap <- left_join(pov, regMap, by = join_by(ireg == DEN_REG))
##### ggiraph ready map
povMap$hCount <- round(povMap$hCount*100, 2)

povhGG <- povMap |> 
  filter(anno == 2000 | anno == 2010 |anno == 2020) |>
  ggplot() +
  geom_sf_interactive(aes(geometry = geometry, fill = hCount, data_id = ireg, tooltip = hCount), colour = 'black') +
  facet_wrap(vars(anno)) +
  labs(x = NULL, y = NULL,
       title = 'Poverty headcount by region',
       caption = 'Data: Bank of Italy. Elaborated by Lorenzo Mattioli - Una Regione per Restare') +
  theme_minimal(base_family = 'Helvetica') +
  scale_fill_viridis_c(direction = -1, limits = c(0, 51), option = 'inferno') +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        panel.grid = element_blank(),
        legend.title = element_blank(),
        plot.title = element_text(size = 15,
                                  hjust = .5),
        plot.subtitle = element_text(hjust = .5),
        plot.caption = element_text(size = 9,
                                    hjust = .5))


##### interactive map
girafe(ggobj = povhGG,
       width_svg = 13,
       options = list(
         opts_hover(css = ''), ## CSS code of line we're hovering over
         opts_hover_inv(css = "opacity:0.3;"), ## CSS code of all other lines
         opts_tooltip(css = "background-color:white;
                      color:black;
                      font-family:Helvetica;
                      font-style:empty;
                      padding:8px;
                      border-radius:10px;",
                      use_cursor_pos = T),
         opts_toolbar(position = 'bottomright')
       ))

Ranking regions by inequality indexes and poverty

# Gini
giniInc |> 
  filter(year == 2000 | year == 2004 | year == 2008 | year == 2012 | year == 2016 | year == 2020) |> 
  drop_na(rank) |> 
  ggplot(aes(x = year, y = rank, data_id = stratum)) +
  geom_bump(linewidth = 0.6, color = 'gray90',
            data = ~. |> filter(stratum != 'Umbria')) +
  geom_bump(aes(colour = stratum), linewidth = 0.8,
            data = ~. |> filter(stratum == 'Umbria' | stratum == 'Lombardia' | stratum == 'Abruzzo')) +
  geom_point(color = 'gray90',
             data = ~. |> filter(stratum != 'Umbria'),
             size = 4) +
  geom_point(aes(colour = stratum),
             data = ~. |> filter(stratum == 'Umbria' | stratum == 'Lombardia' | stratum == 'Abruzzo'),
             size = 4) +
  geom_point(color = 'white', size = 2) +
  geom_text_interactive(aes(label = stratum, group = stratum), colour = 'gray90', x = 2021, hjust = 0, size = 3.5, family = 'Helvetica',
                        data = ~. |> filter(year == 2020)) +
  geom_text(aes(label = stratum, group = stratum), colour = 'black', x = 2021, hjust = 0,, size = 3.5, family = 'Helvetica',
            data = ~. |> filter(year == 2020 & stratum == 'Umbria' | year == 2020 & stratum == 'Lombardia' | year == 2020 & stratum == 'Abruzzo')) +
  scale_color_viridis_d(option = 'mako') +
  scale_x_continuous(limits = c(2000, 2024) ,expand = c(0.01, 0), breaks=c(2000, 2004, 2008, 2012, 2016, 2020)) +
  scale_y_reverse(expand = c(0.02, 0), breaks = c(5, 10, 15, 20)) +
  labs(x = NULL, y = NULL,
       title = 'Classifica delle regioni italiane per indice di Gini',
       subtitle = 'Reddito equivalente familiare',
       caption = 'Dati: Banca d\'Italia. Elaborazione di Lorenzo Mattioli - Una Regione per Restare') +
  theme_minimal(base_family = 'Helvetica') +
  theme(
    legend.position = 'none',
    panel.grid = element_blank(),
    plot.title = element_text(size = 15,
                              hjust = .5),
    plot.subtitle = element_text(hjust = .5),
    plot.caption = element_text(size = 8,
                                hjust = .5)
  )
# Poverty
pov |> 
  filter(anno == 2000 | anno == 2004 | anno == 2008 | anno == 2012 | anno == 2016 | anno == 2020) |> 
  ggplot(aes(x = anno, y = rankHCount, group = ireg, data_id = ireg)) +
    geom_bump(linewidth = 0.6, color = "gray90", smooth = 6) +
    geom_bump(aes(colour = ireg), linewidth = 0.8, smooth = 6,
                data = ~. |> filter(ireg == 'Umbria')) +
    geom_point(color = "gray90", size = 4) +
    geom_point(aes(colour = ireg),
               data = ~. |> filter(ireg == 'Umbria' | ireg == 'Lombardia' | ireg == 'Abruzzo'),
               size = 4) +
    geom_point(color = 'white', size = 2) +
    geom_text(aes(label = ireg, group = ireg), colour = 'gray90', x = 2021, hjust = 0, size = 3.5, family = 'Helvetica',
              data = ~. |> filter(anno == 2020)) +
    geom_text(aes(label = ireg, group = ireg), colour = 'black', x = 2021, hjust = 0,, size = 3.5, family = 'Helvetica',
              data = ~. |> filter(anno == 2020 & ireg == 'Umbria' | anno == 2020 & ireg == 'Lombardia' | anno == 2020 & ireg == 'Abruzzo')) +
    scale_color_manual(values = met.brewer('Degas')) +
    scale_x_continuous(limits = c(2000, 2030) ,expand = c(0.01, 0), breaks=c(2000, 2010, 2020)) +
    scale_y_reverse(expand = c(0.02, 0), breaks = c(1, 5, 10, 15, 20)) +
    labs(x = NULL, y = NULL,
         title = 'Classifica delle regioni italiane per tasso di povertà',
         subtitle = 'Dalla più povera alla meno povera',
         caption = 'Dati: Banca d\'Italia. Elaborazione di Lorenzo Mattioli - Una Regione per Restare') +
    theme_minimal(base_family = 'Helvetica') +
    theme(
      legend.position = 'none',
      panel.grid = element_blank(),
      plot.title = element_text(size = 15,
                           hjust = .5),
      plot.subtitle = element_text(hjust = .5),
      plot.caption = element_text(size = 8,
                                  hjust = .5)
    )

Investigating causes of poverty through LPM modeling

lpmresults$vars <- factor(lpmresults$vars, levels=unique(lpmresults$vars))
lpmresults$roundEst <- round(lpmresults$Estimate, 2)

gglpm <- lpmresults |> 
  filter(vars == 'Titolo di studio') |> 
  ggplot(aes(x=Estimate, y=reg, colour = vars, data_id = roundEst, tooltip = roundEst)) +
  geom_vline(xintercept = 0,
             linewidth = 0.4,
             color = 'gray70') +
  geom_linerange_interactive(aes(xmin=`2.5 %`,xmax=`97.5 %`), linewidth = .6) +
  geom_point_interactive(size = 4) +
  geom_point(colour = 'white', size = 2) +
  geom_point_interactive(aes(x = `2.5 %`), shape = '|', size = 4) +
  geom_point_interactive(aes(x = `97.5 %`), shape = '|', size = 4) +
  labs(x = NULL, y = NULL,
       title = 'Probability of falling below relative poverty line based on head of household\'s educational attainment',
       subtitle = 'Reference category: post-lauream specialisation',
       caption = 'Data: Bank of Italy. Elaborated by Lorenzo Mattioli - Una Regione per Restare') +
  scale_color_manual(values = met.brewer('Degas')) +
  theme_minimal(base_family = 'Helvetica') +
  theme(panel.grid = element_line(),
        legend.position = 'none',
        plot.title = element_text(size = 15,
                                  hjust = 1),
        plot.subtitle = element_text(hjust = -.065),
        plot.caption = element_text(size = 8,
                                    hjust = .5))

girafe(ggobj = gglpm,
       width_svg = 13,
       options = list(
         opts_hover(css = ''), ## CSS code of line we're hovering over
         opts_hover_inv(css = "opacity:0.3;"), ## CSS code of all other lines
         opts_tooltip(css = "background-color:white;
                      color:black;
                      font-family:Helvetica;
                      font-style:empty;
                      padding:8px;
                      border-radius:10px;",
                      use_cursor_pos = T),
         opts_toolbar(position = 'bottomright')))